Another season and my bot finally made it into Champion I. 150 reward cards and ELO of 5277.
Improvement explained
At the start of last season I switched to a regularization approach for my Monte Carlo Tree search. Before that my approach was as follows:
- Pick a team through tree traversal
- Evaluate this team against n teams and sum up the reward function values
My new approach:
- Pick a team through tree traversal
- Evaluate this team against one random team from a pool of n teams
With that change aicu managed to play in the lower Champion I league. Somewhere between rank 90 and 70. Aside from that, I bought the remaining legendary summoners at max level (including Archmage Arius) at the end of this season, which gave aicu a little push as well.
Sure, Aicu is playing now with a 95% max level deck, but how did the changes affect aicu-chan a deck with a couple reward cards and only level 1 Summoners? Well, it pushed all the way to Silver II from Bronze I last season. The corresponding chart is in aicu chan's update post.
Improvements?
Further improvements are starting to get more complicated. For now I'm trying to figure out if I want to follow the "intuition" approach of AlphaZero or try some other things along the lines of temporal difference learning. Need to brainstorm that a bit, the approach of AlphaZero shows great promises in Hearthstone, Go, Chess etc. But I'd actually like to experiment with other approaches before I go down that route. The Trie approach which I explained last time is something I want to explore further.
Giveaway
| Naga Fire Wizard | Naga Fire Wizard | Javelin Thrower | Javelin Thrower | Javelin Thrower |
For this giveaway there's less variety but more Earth and Fire Splinter awesomeness :). Aside from that the same rules apply as last time. Everyone who comments below with a valid SM username get's added to the giveaway pool. Cards are distributed at random at the end.